Fleet Prognosis with Physics-informed Recurrent Neural Networks. 3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation. Recognizing Manipulation Actions from State-Transformations. Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences. Self-Paced Multi-Label Learning wit …

Exploring Language Similarities with Dimensionality Reduction Technique. Practical Deep Learning with Bayesian Principles. MLQA: Evaluating Cross-lingual Extractive Question Answering. Attentive Modality Hopping Mechanism for Speech Emotion Recognition. Temporal Localization of Moments in Video Collections with Natural Language. An Unsupervised Aut …

A Multi-level procedure for enhancing accuracy of machine learning algorithms. Template-free Data-to-Text Generation of Finnish Sports News. On the (In)fidelity and Sensitivity for Explanations. Regression Concept Vectors for Bidirectional Explanations in Histopathology. ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual neTworks. BMN: Boun …

Patent Analytics Based on Feature Vector Space Model: A Case of IoT. Playing log(N)-Questions over Sentences. HAR-Net: Joint Learning of Hybrid Attention for Single-stage Object Detection. Recovering the parameters underlying the Lorenz-96 chaotic dynamics. Machine-learning based three-qubit gate for realization of a Toffoli gate with cQED-based tr …

Forecasting Wireless Demand with Extreme Values using Feature Embedding in Gaussian Processes. Intra-day Equity Price Prediction using Deep Learning as a Measure of Market Efficiency. Validation of a deep learning mammography model in a population with low screening rates. Two-phase flow regime prediction using LSTM based deep recurrent neural netw …

ACE: Artificial Checkerboard Enhancer to Induce and Evade Adversarial Attacks. A Semi-Markov Structured Support Vector Machine Model for High-Precision Named Entity Recognition. Objects365: A Large-Scale, High-Quality Dataset for Object Detection. Entity resolution for noisy ASR transcripts. ON BREIMAN’S DILEMMA IN NEURAL NETWORKS: SUCCESS AND FAIL …